{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Demo of DBSCAN clustering algorithm\n",
    "\n",
    "\n",
    "Finds core samples of high density and expands clusters from them.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "\n",
    "# #############################################################################\n",
    "# Generate sample data\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "X, labels_true = make_blobs(n_samples=1000, centers=centers, cluster_std=0.4,\n",
    "                            random_state=0)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "C=pd.read_csv(r\"C:\\Users\\user\\OneDrive\\code space\\python\\1notebook\\data\\tagData.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=C.values[::1000]\n",
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 2\n",
      "Homogeneity: 0.547\n",
      "Completeness: 0.850\n",
      "V-measure: 0.666\n",
      "Adjusted Rand Index: 0.547\n",
      "Adjusted Mutual Information: 0.546\n",
      "Silhouette Coefficient: 0.439\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Compute    \n",
    "db = DBSCAN(eps=0.3, min_samples=10).fit(X)\n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_\n",
    "\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n",
    "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n",
    "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n",
    "print(\"Adjusted Rand Index: %0.3f\"\n",
    "      % metrics.adjusted_rand_score(labels_true, labels))\n",
    "print(\"Adjusted Mutual Information: %0.3f\"\n",
    "      % metrics.adjusted_mutual_info_score(labels_true, labels))\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(X, labels))\n",
    "\n",
    "# #############################################################################\n",
    "# Plot result\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = [plt.cm.Spectral(each)\n",
    "          for each in np.linspace(0, 1, len(unique_labels))]\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = [0, 0, 0, 1]\n",
    "\n",
    "    class_member_mask = (labels == k)\n",
    "\n",
    "    xy = X[class_member_mask & core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
    "             markeredgecolor='k', markersize=14)\n",
    "\n",
    "    xy = X[class_member_mask & ~core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
    "             markeredgecolor='k', markersize=6)\n",
    "\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.58057881,  0.43199283],\n",
       "       [ 1.70562094,  1.16006288],\n",
       "       [ 0.8016818 , -0.51336891],\n",
       "       ...,\n",
       "       [-0.75715533, -1.41926816],\n",
       "       [-0.34736103, -0.84889633],\n",
       "       [ 0.61103884, -0.46151157]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
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